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%global _empty_manifest_terminate_build 0
Name: python-gekko
Version: 1.0.6
Release: 1
Summary: Machine learning and optimization for dynamic systems
License: MIT
URL: https://github.com/BYU-PRISM/GEKKO
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f9/9e/e34c5eb9943a1e8b089577cbd924a46f51164c0af64fd15e815bd468ca51/gekko-1.0.6.tar.gz
BuildArch: noarch
Requires: python3-numpy
%description
GEKKO is a python package for machine learning and optimization, specializing in
dynamic optimization of differential algebraic equations (DAE) systems. It is coupled
with large-scale solvers APOPT and IPOPT for linear, quadratic, nonlinear, and mixed integer
programming. Capabilities include machine learning, discrete or continuous state space
models, simulation, estimation, and control.
Gekko models consist of equations and variables that create a symbolic representation of the
problem for a single data point or single time instance. Solution modes then create the full model
over all data points or time horizon. Gekko supports a wide range of problem types, including:
- Linear Programming (LP)
- Quadratic Programming (QP)
- Nonlinear Programming (NLP)
- Mixed-Integer Linear Programming (MILP)
- Mixed-Integer Quadratic Programming (MIQP)
- Mixed-Integer Nonlinear Programming (MINLP)
- Differential Algebraic Equations (DAEs)
- Mathematical Programming with Complementarity Constraints (MPCCs)
- Data regression / Machine learning
- Moving Horizon Estimation (MHE)
- Model Predictive Control (MPC)
- Real-Time Optimization (RTO)
- Sequential or Simultaneous DAE solution
Gekko compiles the model into byte-code and provides sparse derivatives to the solver with
automatic differentiation. Gekko includes data cleansing functions and standard tag actions for industrially
hardened control and optimization on Windows, Linux, MacOS, ARM processors, or any other platform that
runs Python. Options are available for local, edge, and cloud solutions to manage memory or compute
resources.
- [Gekko Homepage](https://machinelearning.byu.edu)
- [Gekko Documentation](https://gekko.readthedocs.io/en/latest/)
- [Gekko Examples](https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization)
- [Get Gekko Help on Stack Overflow](https://stackoverflow.com/questions/tagged/gekko)
%package -n python3-gekko
Summary: Machine learning and optimization for dynamic systems
Provides: python-gekko
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-gekko
GEKKO is a python package for machine learning and optimization, specializing in
dynamic optimization of differential algebraic equations (DAE) systems. It is coupled
with large-scale solvers APOPT and IPOPT for linear, quadratic, nonlinear, and mixed integer
programming. Capabilities include machine learning, discrete or continuous state space
models, simulation, estimation, and control.
Gekko models consist of equations and variables that create a symbolic representation of the
problem for a single data point or single time instance. Solution modes then create the full model
over all data points or time horizon. Gekko supports a wide range of problem types, including:
- Linear Programming (LP)
- Quadratic Programming (QP)
- Nonlinear Programming (NLP)
- Mixed-Integer Linear Programming (MILP)
- Mixed-Integer Quadratic Programming (MIQP)
- Mixed-Integer Nonlinear Programming (MINLP)
- Differential Algebraic Equations (DAEs)
- Mathematical Programming with Complementarity Constraints (MPCCs)
- Data regression / Machine learning
- Moving Horizon Estimation (MHE)
- Model Predictive Control (MPC)
- Real-Time Optimization (RTO)
- Sequential or Simultaneous DAE solution
Gekko compiles the model into byte-code and provides sparse derivatives to the solver with
automatic differentiation. Gekko includes data cleansing functions and standard tag actions for industrially
hardened control and optimization on Windows, Linux, MacOS, ARM processors, or any other platform that
runs Python. Options are available for local, edge, and cloud solutions to manage memory or compute
resources.
- [Gekko Homepage](https://machinelearning.byu.edu)
- [Gekko Documentation](https://gekko.readthedocs.io/en/latest/)
- [Gekko Examples](https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization)
- [Get Gekko Help on Stack Overflow](https://stackoverflow.com/questions/tagged/gekko)
%package help
Summary: Development documents and examples for gekko
Provides: python3-gekko-doc
%description help
GEKKO is a python package for machine learning and optimization, specializing in
dynamic optimization of differential algebraic equations (DAE) systems. It is coupled
with large-scale solvers APOPT and IPOPT for linear, quadratic, nonlinear, and mixed integer
programming. Capabilities include machine learning, discrete or continuous state space
models, simulation, estimation, and control.
Gekko models consist of equations and variables that create a symbolic representation of the
problem for a single data point or single time instance. Solution modes then create the full model
over all data points or time horizon. Gekko supports a wide range of problem types, including:
- Linear Programming (LP)
- Quadratic Programming (QP)
- Nonlinear Programming (NLP)
- Mixed-Integer Linear Programming (MILP)
- Mixed-Integer Quadratic Programming (MIQP)
- Mixed-Integer Nonlinear Programming (MINLP)
- Differential Algebraic Equations (DAEs)
- Mathematical Programming with Complementarity Constraints (MPCCs)
- Data regression / Machine learning
- Moving Horizon Estimation (MHE)
- Model Predictive Control (MPC)
- Real-Time Optimization (RTO)
- Sequential or Simultaneous DAE solution
Gekko compiles the model into byte-code and provides sparse derivatives to the solver with
automatic differentiation. Gekko includes data cleansing functions and standard tag actions for industrially
hardened control and optimization on Windows, Linux, MacOS, ARM processors, or any other platform that
runs Python. Options are available for local, edge, and cloud solutions to manage memory or compute
resources.
- [Gekko Homepage](https://machinelearning.byu.edu)
- [Gekko Documentation](https://gekko.readthedocs.io/en/latest/)
- [Gekko Examples](https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization)
- [Get Gekko Help on Stack Overflow](https://stackoverflow.com/questions/tagged/gekko)
%prep
%autosetup -n gekko-1.0.6
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-gekko -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.6-1
- Package Spec generated
|
